March 2023 arXiv papers — page 45

Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif, Muhammad Shafique

Graph neural networks (GNNs) have shown great success in detecting intellectual property (IP) piracy and hardware Trojans (HTs). However, the machine learning community has demonstrated that GNNs are susceptible to data poisoning attacks, which result in GNNs performing abnormally on graphs with pre-defined backdoor triggers (realized using crafted subgraphs

Eduard Alarcón, Sergi Abadal, Fabio Sebastiano, Masoud Babaie

The grand challenge of scaling up quantum computers requires a full-stack architectural standpoint. In this position paper, we will present the vision of a new generation of scalable quantum computing architectures featuring distributed quantum cores (Qcores) interconnected via quantum-coherent qubit state transfer links and orchestrated via an integrated wi

Deborah Morgan, Youmna Hashem, John Francis, Saba Esnaashari

This article explores how the 'rules in use' from Ostrom's Institutional Analysis and Development Framework (IAD) can be developed as a context analysis approach for AI. AI risk assessment frameworks increasingly highlight the need to understand existing contexts. However, these approaches do not frequently connect with established institutional analysis sch

William Won, Taekyung Heo, Saeed Rashidi, Srinivas Sridharan

As deep learning models and input data are scaling at an unprecedented rate, it is inevitable to move towards distributed training platforms to fit the model and increase training throughput. State-of-the-art approaches and techniques, such as wafer-scale nodes, multi-dimensional network topologies, disaggregated memory systems, and parallelization strategie

Chi Xie, Fangao Zeng, Yue Hu, Shuang Liang

Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning. Such queries are explicitly associated to interaction categories, converted to image specific category representation via a transformer decoder, and learnt via an auxiliary image-level classificat

Raghavendra Ramachandra, Sushma Venkatesh, Gaurav Jaswal, Guoqiang Li

Face morphing attacks have emerged as a potential threat, particularly in automatic border control scenarios. Morphing attacks permit more than one individual to use travel documents that can be used to cross borders using automatic border control gates. The potential for morphing attacks depends on the selection of data subjects (accomplice and malicious ac

Jipeng Yan, Biao Huang, Johanna Tonko, Matthieu Toulemonde

Micro-vascular flow in the myocardium is of significant importance clinically but remains poorly understood. Up to 25% of patients with symptoms of coronary heart diseases have no obstructive coronary arteries and have suspected microvascular diseases. However, such microvasculature is difficult to image in vivo with existing modalities due to the lack of re

Titouan Carette, Jan Głowacki, Leon Loveridge

Quantum reference frames are needed in quantum theory for much the same reasons that reference frames are in classical theories: to manifest invariance in line with fundamental relativity principles and to provide a basis for the definition of observable quantities. Though around since the 1960s, and used in a wide range of applications, only recently has th

Linning Xu, Yuanbo Xiangli, Sida Peng, Xingang Pan

Purely MLP-based neural radiance fields (NeRF-based methods) often suffer from underfitting with blurred renderings on large-scale scenes due to limited model capacity. Recent approaches propose to geographically divide the scene and adopt multiple sub-NeRFs to model each region individually, leading to linear scale-up in training costs and the number of sub

Stéphane Louboutin

As a consequence of their work, Bruce C. Berndt and Ronald J. Evans in 1977 and Larry Joel Goldstein and Michael Razar in 1976 obtained a formula for the square of the class number of an imaginary quadratic number field in terms of Dedekind sums. Here we focus only on this formula and give a new short and simple proof of it. We finally express the relative c

J. Grefe, P. Herre, Y. Hilgers, F. Labbus

We present a detailed study of the electronic and magnetic ground state properties of Fe$_{1-x}$Co$_{x}$Si using a combination of macroscopic and microscopic experimental techniques. From these experiments we quantitatively characterize the metal-insulator transition and magnetic/non-magnetic quantum phase transition occurring at low doping levels in Fe$_{1-

Omar Rifki, Thierry Garaix

We develop an asymptotic approximation and bounds for the traveling salesman problem with time slots, i.e. when the time windows of points to visit are a partition of a given time horizon. Although this problem is relevant in several delivery applications, operational research researchers did not pay close attention to it, contrary to the extensively studied

Richard Petri, Grace Li Zhang, Yiran Chen, Ulf Schlichtmann

Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the large number of multiply-and-accumulate (MAC) operations. To address this challenge, we propose PowerPruning, a novel method to reduce power consumption in digital neural network accel

Paulo Amaral, Silvia Carbonell-Sala, Francisco M. De La Vega, Tiago Faial

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dr

Yong Hyun Ahn, Gyeong-Moon Park, Seong Tae Kim

It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-distribution (OOD) data are likely to cause big problems. OOD detection problem fundamentally begins in that the model cannot express what it is not aware of. Post-hoc OOD detectio

Nieves Montes, Georgina Curto, Nardine Osman, Carles Sierra

The deceleration of global poverty reduction in the last decades suggests that traditional redistribution policies are losing their effectiveness. Alternative ways to work towards the #1 United Nations Sustainable Development Goal (poverty eradication) are required. NGOs have insistingly denounced the criminalization of poverty, and the social science litera

Emilien Flayac, Girish Nair, Iman Shames

This paper proposes an algorithm that combines Fast Moving Horizon Parameter Estimation and Model Predictive Control subject to an observability constraint designed to ensure a lower bound on the performance of the parameter estimator. Output-feedback stability is proved through input-to-state stability of the state/error system under a small noise and initi

Wenqing Li, Yue Wang, Muhammad Shafique, Saif Eddin Jabari

Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers. However, it is still unclear how these triggers can be activated while adhering to traffic principles. Understanding this vulnerability in a dynamic traffic environment is crucial. This work

Shengzhe Pan, Zhaohan Zhang, Chenxi Hu, Peifen Lu

Recent advances in laser technology have enabled tremendous progress in photochemistry, at the heart of which is the breaking and formation of chemical bonds. Such progress has been greatly facilitated by the development of accurate quantum-mechanical simulation method, which, however, does not necessarily accompany clear dynamical scenarios and is rather of

O. Iskra, S. Dashkovskiy, A. Pokutniy, V. Slynko

The paper is devoted to obtaining conditions for the roughness of dichotomy in the Banach spaces. Deep analysis of the well known papers was considered. The main results also works for the case of unbounded operators.

Martin Křepela, Zdeněk Mihula, Javier Soria

We characterize the rearrangement-invariant hull, with respect to a given measure $\mu$, of weighted Lebesgue spaces. The solution leads us to first consider when this space is contained in the sum of $(L^1 + L^\infty)(R, \mu)$ and the final condition is given in terms of embeddings for weighted Lorentz spaces.

Jonas Becker, Jan Philip Wahle, Terry Ruas, Bela Gipp

The growing prominence of large language models, such as GPT-4 and ChatGPT, has led to increased concerns over academic integrity due to the potential for machine-generated content and paraphrasing. Although studies have explored the detection of human- and machine-paraphrased content, the comparison between these types of content remains underexplored. In t

Thilo Hagendorff, Ishita Dasgupta, Marcel Binz, Stephanie C. Y. Chan

Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains. Understanding their behavior and reasoning abilities therefore holds significant importance. We argue that a fruitful direction for research is engaging LLMs in behavioral experiments inspired by psychology that have tradi

François Copie, Pierre Suret, Stéphane Randoux

We present experiments performed in a recirculating fiber loop in which we realize the single-shot observation of the space and time interaction of two and three bright solitons. The space-time evolutions observed in experiments provide clear evidence of a nearly-integrable nonlinear wave dynamics that can be easily interpreted within the framework of the in

Bikun Wang, Zhipeng Wang, Chenhao Zhu, Zhiqiang Zhang

Learning-based approaches have achieved remarkable performance in the domain of autonomous driving. Leveraging the impressive ability of neural networks and large amounts of human driving data, complex patterns and rules of driving behavior can be encoded as a model to benefit the autonomous driving system. Besides, an increasing number of data-driven works

Artbazar Galtbayar, Kenji Yajima

We prove that the wave operators of scattering theory for the fourth order Schr\"odinger operators $\Delta^2 + V(x)$ in ${\mathbb R}^4$ are bounded in $L^p({\mathbb R}^4)$ for the set of $p$'s of $(1,\infty)$ depending on the kind of spectral singularities of $H$ at zero which can be described by the space of bounded solutions of $(\Delta^2 + V(x))u(x)=0$.

Alfred O. Achieng, George B. Arhonditsis, Nicholas E. Mandrack, Catherine M. Febria

Africa is experiencing extensive biodiversity loss due to rapid changes in the environment, where natural resources constitute the main instrument for socioeconomic development and a mainstay source of livelihoods for an increasing population. Lack of data and information deficiency on biodiversity, but also budget constraints and insufficient financial and

Cédric Arhancet, Christoph Kriegler, Christian Le Merdy, Safoura Zadeh

Let $G$ be a locally compact unimodular group, let $1\leq p<\infty$,let $\phi\in L^\infty(G)$ and assume that the Fourier multiplier $M_\phi$associated with $\phi$ is bounded on the noncommutative $L^p$-space $L^p(VN(G))$.Then $M_\phi\colon L^p(VN(G))\to L^p(VN(G))$ is separating (that is,$\{a^*b=ab^*=0\}\Rightarrow\{M_\phi(a)^* M_\phi(b)=M_\phi(a)M_\phi(b)^

Yusuke Koyama, Kazuya Fujimoto, Shuta Nakajima, Yuki Kawaguchi

A quantum kicked rotor model is one of the promising systems to realize various Floquet topological phases. We consider a double-kicked rotor model for a one-dimensional quasi-spin-1/2 Bose-Einstein condensate with spin-dependent and spin-independent kicks which are implementable for cold atomic experiments. We theoretically show that the model can realize a

Jimmi Agerskov, Kristian Nielsen, Christian Marius Lillelund, Christian Fischer Pedersen

An abundance of information about cancer exists online, but categorizing and extracting useful information from it is difficult. Almost all research within healthcare data processing is concerned with formal clinical data, but there is valuable information in non-clinical data too. The present study combines methods within distributed computing, text retriev

Rainey Lyons, Emilio N. M. Cirillo, Adrian Muntean

We study a nonlinear coupled parabolic system with non-local drift terms modeling at the continuum level the inter-species interaction within a ternary mixture that allows the evaporation of one of the species. In the absence of evaporation, the proposed system coincides with the hydrodynamic limit of a stochastic interacting particle system of Blume-Capel-t

Jin-han Guo, Yi-wei Ni, Ze Zhong, Yang Guo

Solar filament eruptions, flares and coronal mass ejections (CMEs) are manifestations of drastic release of energy in the magnetic field, which are related to many eruptive phenomena from the Earth magnetosphere to black hole accretion disks. With the availability of high-resolution magnetograms on the solar surface, observational data-based modelling is a p

Piotr Lebiedowicz, Otto Nachtmann, Antoni Szczurek

We evaluate the cross section for diffractive bremsstrahlung of a single photon in the $pp \to pp \gamma$ reaction at high energies and at forward photon rapidities. Several differential distributions, for instance, in $y$, $k_{\perp}$ and $\omega$, the rapidity, the absolute value of the transverse momentum, and the energy of the photon, respectively, are p

G. Mophou, M. Moutamal, M. Warma

We are concerned with a space-time fractional parabolic initial-boundary value problem of Sturm Liouville type in a general star graph with mixed Dirichlet and Neumann boundary controls. We first give several existence, uniqueness and regularity results of weak and very-weak solutions. Using the notion of no-regret control introduced by Lions, we prove the e

Damian Gaska, Jerzy Margielewicz, Grzegorz Litak, Piotr Wolszczak

The paper presents the application of a new impulse excitation diagram (IED) to help realize high-energy orbits in nonlinear energy harvesting systems. In the case of non-linearity, we can deal with the occurrence of coexisting solutions and the proposed diagram allows for the use of the impulse excitation method in order to change the solution. For this pur

Johnathon Barbish, Mark Paul

We explore the high dimensional chaos of a one-dimensional lattice of diffusively coupled tent maps using the covariant Lyapunov vectors (CLVs). We investigate the connection between the dynamics of the maps in the physical space and the dynamics of the covariant Lyapunov vectors and covariant Lyapunov exponents that describe the direction and growth (or dec

Zhi-Gang Wang, Fei Lu, Yang Liu

We construct the $\Sigma$-type currents to investigate the D-wave charmed baryon states with the QCD sum rules systematically. The predicted masses $M=3.35^{+0.13}_{-0.18}\,\rm{GeV}$ ($3.33^{+0.13}_{-0.16}\,\rm{GeV}$), $3.34^{+0.14}_{-0.18}\,\rm{GeV}$ ($3.35^{+0.13}_{-0.16}\,\rm{GeV}$) and $3.35^{+0.12}_{-0.13}\,\rm{GeV}$ ($3.35^{+0.12}_{-0.14}\,\rm{GeV}$) f

Pierre-Yves Genest, Pierre-Edouard Portier, Elöd Egyed-Zsigmond, Laurent-Walter Goix

Unsupervised Relation Extraction (RE) aims to identify relations between entities in text, without having access to labeled data during training. This setting is particularly relevant for domain specific RE where no annotated dataset is available and for open-domain RE where the types of relations are a priori unknown. Although recent approaches achieve prom

Jean-Bernard Lasserre

We show that Bernstein polynomials are related to the Lebesgue measure on [0, 1] in a manner similar as Chebyshev polynomials are related to the equilibrium measure of [--1, 1]. We also show that Pell's polynomial equation satisfied by Chebyshev polynomials, provides a partition of unity of [--1, 1], the analogue of the partition of unity of [0, 1] provided

Nitish Shukla, Anurima Dey, Srivatsan K

Manufacturing wafers is an intricate task involving thousands of steps. Defect Pattern Recognition (DPR) of wafer maps is crucial for determining the root cause of production defects, which may further provide insight for yield improvement in wafer foundry. During manufacturing, various defects may appear standalone in the wafer or may appear as different co

Damiano Rossi

We study the Brown complex associated to the poset of $\ell$-subgroups in the case of a finite reductive group defined over a field $\mathbb{F}_q$ of characteristic prime to $\ell$. First, under suitable hypotheses, we show that its homotopy type is determined by the generic Sylow theory developed by Brou\'e and Malle and, in particular, only depends on the

Tim Yarally, Luís Cruz, Daniel Feitosa, June Sallou

Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of Green AI to consider energy consumption as a metric of equal importance to accuracy and to reduce any irrelevan

Jerzy Margielewicz, Damian Gaska, Grzegorz Litak, Tomasz Haniszewski

Dynamical systems for kinetic energy harvesting have recently been designed that use nonlinear mechanical resonators and corresponding transducers to create electrical output. Nonlinearities are important for providing a broadband input frequency efficiency, which is necessary to ensure that the output voltage is at a satisfactory level in the case of a vari

Alain Haraux

We show how it is possible to define and study black holes of arbitrary shapes in the framework of Newtonian mechanics.

Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette

With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the real environment. However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering is difficult or when obtaining reward an

Rahul Mondal, Nesar Hossain

The idea of rough statistical convergence for double sequences was studied by Ozcan and Or[29] in a intuitionistic fuzzy normed space. Recently the same has been generalized in the ideal context by Hossain and Banerjee[15] for sequences. Here in this paper we have discussed the idea of rough ideal convergence of double sequences in intuitionistic fuzzy norme

Erwan Faou, Yoann Le Henaff, Pierre Raphaël

Based on recent ideas, stemming from the use of bubbles, we discuss an algorithm for the numerical simulation of the cubic nonlinear Schr{\"o}dinger equation with harmonic potential in any dimension, which could be easily extended to other polynomial nonlinearities. For the linear part of the equation, the algorithm consists in discretizing the initial funct

Marcin Kępa, Niels Focke, Łukasz Cywiński, Jan. A. Krzywda

Due to presence of magnetic field gradient needed for coherent spin control, dephasing of single-electron spin qubits in silicon quantum dots is often dominated by $1/f$ charge noise. We investigate theoretically fluctuations of ground state energy of an electron in gated quantum dot in realistic Si/SiGe structure. We assume that the charge noise is caused b

Marc Lambert, Silvère Bonnabel, Francis Bach

We consider the problem of computing a Gaussian approximation to the posterior distribution of a parameter given a large number N of observations and a Gaussian prior, when the dimension of the parameter d is also large. To address this problem we build on a recently introduced recursive algorithm for variational Gaussian approximation of the posterior, call

Thành Nguyen, Alexander Teytelboym, Shai Vardi

We study a model of dynamic combinatorial assignment of indivisible objects without money. We introduce a new solution concept called ``dynamic approximate competitive equilibrium from equal incomes'' (DACEEI), which stipulates that markets must approximately clear in almost all time periods. A naive repeated application of approximate competitive equilibriu

Martin Keller-Ressel, Felix Sachse

Using the concept of envelopes we show how to divide the state space $\RR^2$ of the two-factor Vasicek model into regions of identical term-structure shape. We develop a formula for determining the shapes utilizing winding numbers and give a nearly complete classification of the parameter space regarding the occurring shapes.

Rashmi Kushwaha, Shreyas Kulkarni, Yatindra Nath Singh

We present a generalized distance metric that can be used to implement routing strategies and identify routing table entries to reach the root node for a given key, in a DHT (Distributed Hash Table) network based on either Chord, Kademlia, Tapestry, or Pastry. The generalization shows that all the above four DHT algorithms are in fact, the same algorithm but

Hashem Ghanem, Samuel Vaiter, Nicolas Keriven

A common issue in graph learning under the semi-supervised setting is referred to as gradient scarcity. That is, learning graphs by minimizing a loss on a subset of nodes causes edges between unlabelled nodes that are far from labelled ones to receive zero gradients. The phenomenon was first described when optimizing the graph and the weights of a Graph Neur

L. Baudis, Y. Biondi, A. Bismark, A. P. Cimental Chavez

There is a compelling physics case for a large, xenon-based underground detector devoted to dark matter and other rare-event searches. A two-phase time projection chamber as inner detector allows for a good energy resolution, a three-dimensional position determination of the interaction site and particle discrimination. To study challenges related to the con

Yuan Zhuang, Binliang Wang, Jianzhu Huai, Miao Li

Millimeter wave radar can measure distances, directions, and Doppler velocity for objects in harsh conditions such as fog. The 4D imaging radar with both vertical and horizontal data resembling an image can also measure objects' height. Previous studies have used 3D radars for ego-motion estimation. But few methods leveraged the rich data of imaging radars,

Benjamin Dörich, Patrick Henning

In this work, we study discrete minimizers of the Ginzburg-Landau energy in finite element spaces. Special focus is given to the influence of the Ginzburg-Landau parameter $\kappa$. This parameter is of physical interest as large values can trigger the appearance of vortex lattices. Since the vortices have to be resolved on sufficiently fine computational me

Brennan C Kahan, Bryan Blette, Michael Harhay, Scott Halpern

Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned to the study's objectives. Cluster randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster specific, and whether they are pa

Bohan Li, Yasheng Sun, Zhujin Liang, Dalong Du

3D semantic scene completion (SSC) is an ill-posed perception task that requires inferring a dense 3D scene from limited observations. Previous camera-based methods struggle to predict accurate semantic scenes due to inherent geometric ambiguity and incomplete observations. In this paper, we resort to stereo matching technique and bird's-eye-view (BEV) repre

Rajni Bala, Sooryansh Asthana, V. Ravishankar

Secure quantum communication protocols based on a prepare-and-measure scheme employ mutually unbiased bases. In these protocols, many runs, in which different participants measure in different bases, simply go wasted. In this paper, we show that it is possible to reduce the number of such runs by a suitable design of the key generation rule. This results in

Michael Hersche, Aleksandar Terzic, Geethan Karunaratne, Jovin Langenegger

Distributed sparse block codes (SBCs) exhibit compact representations for encoding and manipulating symbolic data structures using fixed-width vectors. One major challenge however is to disentangle, or factorize, the distributed representation of data structures into their constituent elements without having to search through all possible combinations. This

Yukihiro Tsuzuki

We propose a financial market model that comprises a savings account and a stock. The stock price process is modeled as a one-dimensional diffusion, in which two types of agents exist: an ordinary investor and a fundraiser who buys or sells stocks as funding activities. Although the investor information is the natural filtration of the diffusion, the fundrai

Kun He, Xin Liu, Yichen Yang, Zhou Qin

Adversarial training has been demonstrated to be the most effective approach to defend against adversarial attacks. However, existing adversarial training methods show apparent oscillations and overfitting issue in the training process, degrading the defense efficacy. In this work, we propose a novel framework, termed Parameter Interpolation based Adversaria

Thomas B. Rolinger, Christopher D. Krieger, Alan Sussman

Irregular memory access patterns pose performance and user productivity challenges on distributed-memory systems. They can lead to fine-grained remote communication and the data access patterns are often not known until runtime. The Partitioned Global Address Space (PGAS) programming model addresses these challenges by providing users with a view of a distri

Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao

Both indoor and outdoor environments are inherently structured and repetitive. Traditional modeling pipelines keep an asset library storing unique object templates, which is both versatile and memory efficient in practice. Inspired by this observation, we propose AssetField, a novel neural scene representation that learns a set of object-aware ground feature

Jiafeng Cui, Xieyuanli Chen

Place recognition is an essential and challenging task in loop closing and global localization for robotics and autonomous driving applications. Benefiting from the recent advances in deep learning techniques, the performance of LiDAR place recognition (LPR) has been greatly improved. However, current deep learning-based methods suffer from two major problem

Jiale Gao, Qingwen Wang, Kezheng Zuo, Jiabao Wu

This paper is aimed to identify some new characterizations and representations of the Minkowski inverse in Minkowski space. First of all, a few representations of {1,3m}, {1,2,3m}, {1,4m} and {1,2,4m}-inverses are given in order to represent the Minkowski inverse. Secondly, some famous characterizations of the Moore-Penrose inverse are extended to that of th

Anirban Biswas, Mainak Chakraborty, Sarif Khan

Indirect searches of Dark Matter (DM), in conjugation with `missing track searches' at the collider seem to confine SU(2)$_L$ fermion triplet DM (FTDM) mass within a narrow range around 1 TeV. The canonical picture of the pure FTDM is in tension since it is under-abundant for the said mass range. Several preceding studies have reported that an extra species

Katharina Gallmeier, J. Xavier Prochaska, Peter C. Cornillon, Dimitris Menemenlis

We extracted ~2.8M nearly cloud-free 144x144 km^2 cutout images from the 2012-2020 Level-2 VIIRS Sea Surface Temperature (SST) dataset to quantitatively compare with MIT ocean general circulation model outputs, specifically the one year LCC4320 1/48 deg global-ocean simulation starting on November 17, 2011, matched in geography and day-of-year to VIIRS obser

Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, Francesco Osborne

With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence,

Carlos Simpson

Let $X=\overline{X}-D$ be a smooth quasi-projective curve. In arXiv:2110.12300 we constructed a Deligne-Hitchin modui space with Hecke gauge groupoid for connections of rank $2$. We extend this construction to the case of any rank $r$, although still keeping a genericity hypothesis. The formal neighborhood of a preferred section corresponding to a tame harmo

Thiago Mergulhão, Florian Beutler, John A. Peacock

Understanding the universe in its pristine epoch is crucial in order to obtain a concise comprehension of the late-time universe. Although current data in cosmology are compatible with Gaussian primordial perturbations whose power spectrum follows a nearly scale-invariant power law, this need not be the case when a fundamental theoretical construction is ass

Ludwig A. Hothorn

Analysis of variance (ANOVA) reveals some disadvantages, such as non-robustness against heteroscedastic or non-normal errors and using difference to overall mean as effect sizes only. As an alternative the multiple contrast test comparing to the overall mean is proposed for 7 effect sizes: ratio-to-OM, quantiles for both ratio or differences, odds ratios for

Jiale Gao, Qingwen Wang, Kezheng Zuo

This paper first introduces a new generalized inverse in Minkowski space, called the m-DMP inverse, and discusses its algebraic and geometrical properties. The second objective is to characterize the m-DMP inverse equivalently by ranges, null spaces and matrix equations, and show its integral and limiting representations and several explicit expressions. Fin

Adejoke T. Odebade, Elhadj Benkhelifa

Cyber security initiatives provide immense opportunities for governments to educate, train, create awareness, and promote cyber hygiene among businesses and the general public. Creating and promoting these initiatives are necessary steps governments take to ensure the cyber health of a nation. To ensure users are safe and confident, especially online, the UK

Agissilaos G. Athanassoulis, Irene Kyza

The nonlinear Schr\"odinger equation is widely used as an approximate model for the evolution in time of the water wave envelope. In the context of simulating ocean waves, initial conditions are typically generated from a measured power spectrum using the random phase approximation, and periodized on an interval of length $L$. It is known that most realistic

Bin Wang, Weichao Sun Hossein Safari, S. Kaveh Hedayati, Jadze P. C. Narag

Tomographic volumetric 3D-printing (TVP) utilizes a nonlinear photoresponse of polymer precursor to cure all points in a three-dimensional (3D) object in parallel. A key challenge in TVP is to build up dose contrast between in-part and out-of-part points in a lateral plane, which relies on coordinated illumination from various projecting angles. This challen

Tomasz Szołdra, Marcelo F. Ciappina, Nicholas Werby, Philip H. Bucksbaum

Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we test CNNs on strong-field ionization photoelectron spectra, training on theoretical data sets to `invert' experimental da

Hanxue Liang, Tianhao Wu, Param Hanji, Francesco Banterle

Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using image quality metrics such as PSNR, SSIM, or LPIPS. There has

Qinglin Zhang, Chong Deng, Jiaqing Liu, Hai Yu

Listening to long video/audio recordings from video conferencing and online courses for acquiring information is extremely inefficient. Even after ASR systems transcribe recordings into long-form spoken language documents, reading ASR transcripts only partly speeds up seeking information. It has been observed that a range of NLP applications, such as keyphra

Adejoke T. Odebade, Elhadj Benkhelifa

This study compares the National Cybersecurity Strategies (NCSSs) of publicly available documents of ten nations across Europe (United Kingdom, France, Lithuania, Estonia, Spain, and Norway), Asia-Pacific (Singapore and Australia), and the American region (the United States of America and Canada). The study observed that there is not a unified understanding

Lukas Ehrke, John Andrew Raine, Knut Zoch, Manuel Guth

We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of particle physics decays and the flexibility of message passing graph neural networks. The Topograph not only solves the combinatoric assignment of observed final state objects, ass

Ritam Majumdar, Shirish Karande, Lovekesh Vig

The spread of many infectious diseases is modeled using variants of the SIR compartmental model, which is a coupled differential equation. The coefficients of the SIR model determine the spread trajectories of disease, on whose basis proactive measures can be taken. Hence, the coefficient estimates must be both fast and accurate. Shaier et al. in the paper "

Christopher Bull, Ahmed Kharrufa

The software development industry is amid another disruptive paradigm change - adopting the use of generative AI (GAI) assistants for programming. Whilst AI is already used in various areas of software engineering, GAI technologies, such as GitHub Copilot and ChatGPT, have ignited peoples' imaginations (and fears). It is unclear how the industry will adapt,

Yu Tang Liu, Aamir Ahmad

Deep reinforcement learning (DRL) frameworks are increasingly used to solve high-dimensional continuous control tasks in robotics. However, due to the lack of sample efficiency, applying DRL for online learning is still practically infeasible in the robotics domain. One reason is that DRL agents do not leverage the solution of previous tasks for new tasks. R

Sebastian Gorgon, Kuo Lv, Jeannine Grüne, Bluebell H. Drummond

Molecules present a versatile platform for quantum information science, and are candidates for sensing and computation applications. Robust spin-optical interfaces are key to harnessing the quantum resources of materials. To date, carbon-based candidates have been non-luminescent, which prevents optical read-out. Here we report the first organic molecules di

Ye Mao, Lan Jiang, Xi Chen, Chao Li

Multi-contrast magnetic resonance imaging (MRI) is the most common management tool used to characterize neurological disorders based on brain tissue contrasts. However, acquiring high-resolution MRI scans is time-consuming and infeasible under specific conditions. Hence, multi-contrast super-resolution methods have been developed to improve the quality of lo

Qinglin Zhang, Chong Deng, Jiaqing Liu, Hai Yu

ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings. MUG includes five tracks, including topic segmentation, topic-level and sess

Marwa Dhiaf, Ahmed Cheikh Rouhou, Yousri Kessentini, Sinda Ben Salem

The Transformer has quickly become the dominant architecture for various pattern recognition tasks due to its capacity for long-range representation. However, transformers are data-hungry models and need large datasets for training. In Handwritten Text Recognition (HTR), collecting a massive amount of labeled data is a complicated and expensive task. In this

Minh-Ngoc Tran, Paco Tseng, Robert Kohn

The Mean Field Variational Bayes (MFVB) method is one of the most computationally efficient techniques for Bayesian inference. However, its use has been restricted to models with conjugate priors or those that require analytical calculations. This paper proposes a novel particle-based MFVB approach that greatly expands the applicability of the MFVB method. W

Yang Zuo, Yu Tang Liu, Aamir Ahmad

Due to their superior energy efficiency, blimps may replace quadcopters for long-duration aerial tasks. However, designing a controller for blimps to handle complex dynamics, modeling errors, and disturbances remains an unsolved challenge. One recent work combines reinforcement learning (RL) and a PID controller to address this challenge and demonstrates its

Federico Paolucci

We present and theoretically analyse the performance of an innovative non-local superconducting single-photon detector. The device operates thanks to the energy-to-phase conversion mechanism, where the energy of the absorbed single-photon is transformed in a variation of the superconducting phase. To this scope, the detector is designed in the form of a doub

C. Carlé, S. Keshavarzi, A. Mursa, P. Karvinen

The stability and accuracy of atomic devices can be degraded by the evolution of their cell inner atmosphere. Hence, the undesired entrance or leakage of background or buffer gas, respectively, that can permeate through the cell walls, should be slowed down. In this work, we investigate helium permeation in microfabricated alkali vapor cells filled with He a

Takahiro Hasebe, Yuki Ueda

Belinschi et al. [Adv. Math., 226 (2011), 3677--3698] proved that the normal distribution is freely infinitely divisible. This paper establishes a certain monotonicity, real analyticity and asymptotic behavior of the density of the free L\'{e}vy measure. The monotonicity property strengthens the result in Hasebe et al. [Int. Math. Res. Not. (2019), 1758--178

Morris Brooks, Sascha Lill

We give a mathematically precise review of a diagrammatic language introduced by Friedrichs in order to simplify computations with creation and annihilation operator products. In that language, we establish explicit formulas and algorithms for evaluating bosonic and fermionic commutators. Further, as an application, we demonstrate that the non-linear Hartree

E. Bozzo, D. P. Huenemoerder, N. Produit, M. Falanga

Capella is the brightest chromospherically active binary in the sky, hosting a cooler G8III giant (Aa) and an hotter G1III companion (Ab). The source has been extensively observed in the X-rays in the past decades not only for its astrophysical interest in the field of corona sources, but also for in-flight calibrations of space-based X-ray instruments. In 2

Laura Grave de Peralta, Alexander Kolpakov

The present note concerns the "graph of graphs" that has cubic graphs as vertices connected by edges represented by the so-called Whitehead moves. Here, we prove that the outer-conductance of the graph of graphs tends to zero as the number of vertices tends to infinity. This answers a question of K. Rafi in the negative.

Yao Junxiang, Remko Fermin, Mariona Cabero, Kaveh Lahabi

In the area of superconducting spintronics, spin triplet supercurrents in half-metallic ferromagnets (HMFs) could yield dissipationless spin transport over large distances, and high current density. Promising among the HMFs is the perovskite oxide La0.7Sr0.3MnO3 (LSMO), and recent studies in combination with the high-Tc superconductor YBa2Cu3O7, or the conve

Raul P. Aristides, Hilda A. Cerdeira

Synchronization has attracted the interest of many areas where the systems under study can be described by complex networks. Among such areas is neuroscience, where is hypothesized that synchronization plays a role in many functions and dysfunctions of the brain. We study the linear stability of synchronized states in networks of Izhikevich neurons using Mas

Hugo Duminil-Copin, Ivailo Hartarsky

We study two-dimensional critical bootstrap percolation models. We establish that a class of these models including all isotropic threshold rules with a convex symmetric neighbourhood, undergoes a sharp metastability transition. This extends previous instances proved for several specific rules. The paper supersedes a draft by Alexander Holroyd and the first

Ryohei Suzuki, Iifan Tyou, Shigenori Ohashi, Kazutoshi Sasahara

The emergence of Consumer-to-Consumer (C2C) platforms has allowed consumers to buy and sell goods directly, but it has also created problems, such as commodity fraud and fake reviews. Trust Management Algorithms (TMAs) are expected to be a countermeasure to detect fraudulent users. However, it is unknown whether TMAs are as effective as reported as they are